Simulation prediction method and device, equipment, storage medium and product

Abstract

The invention discloses a simulation prediction method and device, equipment, a storage medium and a product, and relates to the technical field of hydraulic engineering, and the method comprises the steps: obtaining a water flow prediction curve according to the actual or designed water inflow turbidity, and the water flow prediction curve is used for representing the change condition of the water outflow velocity along with time under the actual or designed water inflow turbidity; according to the water flow prediction curve and an HYDRUS model, a water quality prediction curve is obtained, and the water quality prediction curve is used for representing the change condition of the effluent turbidity along with time under the actual or designed inflow turbidity. According to the method, the outlet water flow velocity is simulated based on the actual or designed inlet water turbidity, and then the predicted outlet water flow velocity result is input into the HYDRUS model as a change condition to predict the corresponding outlet water turbidity, so that the simultaneous simulation prediction of the water volume and the water quality of the catchment gallery project is realized.

Description

Technical Field

[0001] The present application relates to the field of water conservancy engineering technology, and in particular to a simulation prediction method, device, equipment, storage medium and product. Background Art

[0002] Among related technologies, water collection corridors are increasingly being applied in various forms to water supply projects in water-scarce regions, driven by the dual impacts of climate change and human water demand. To better understand the operational effectiveness of water collection corridors, numerical simulation methods (such as MODFLOW and Fluent) have also been widely used in water collection corridor research. However, MODFLOW models can only be applied to saturated aquifers, while the hydraulic characteristics of water collection corridor filtration systems are mostly unsaturated, resulting in limited simulation accuracy. Fluent is also primarily used to simulate flow fields (water volume) within media, making its application in actual water collection corridor projects difficult.

[0003] Therefore, how to more accurately simulate the water volume and water quality of water collection corridor projects is an urgent problem to be solved. Summary of the Invention

[0004] The main purpose of this application is to provide a simulation prediction method, device, equipment, storage medium and product, aiming to solve the technical problem of how to more accurately simulate the water volume and water quality of water collection corridor projects.

[0005] To achieve the above objectives, this application proposes a simulation prediction method, which includes: Obtaining a water flow prediction curve based on the actual or designed inlet turbidity and the corresponding partial outlet flow rate monitoring data, wherein the water flow prediction curve is used to characterize the change of the outlet flow rate over time under the actual or designed inlet turbidity; According to the water flow prediction curve and the HYDRUS model, a water quality prediction curve is obtained, where the water quality prediction curve is used to characterize the change of the outlet turbidity over time under the actual or designed inlet turbidity.

[0006] In one embodiment, obtaining a water flow prediction curve based on actual or designed influent turbidity includes: A water flow prediction curve is obtained based on the partial outlet water flow rate monitoring data under the actual or designed inlet water turbidity, wherein the water flow prediction curve is obtained by fitting the partial outlet water flow rate monitoring data in a curve regression model.

[0007] In some embodiments, when the curve regression model is an exponential function model, the expression of the curve regression model is: ; in, for The water flow rate corresponding to the moment, is the initial outlet flow rate under the actual or designed inlet turbidity, e is the Napier constant, For time, It is a target coefficient that is positively correlated with the actual or designed influent turbidity.

[0008] In some embodiments, the target coefficient is obtained by the following expression: ; in, is the actual or designed influent turbidity.

[0009] In some embodiments, before obtaining the water quality prediction curve based on the water flow prediction curve and the HYDRUS model, the method further includes: Obtaining effluent turbidity monitoring data under the actual or designed influent turbidity; The water flow prediction curve under the actual or designed inlet turbidity is input into the HYDRUS model, and combined with the effluent turbidity monitoring data under the actual or designed inlet turbidity, the effluent turbidity simulation curve under the actual or designed inlet turbidity is obtained.

[0010] In addition, to achieve the above-mentioned purpose, the present application also proposes a simulation prediction device, which includes: A first curve fitting module is used to obtain a water flow prediction curve according to the actual or designed inlet turbidity, where the water flow prediction curve is used to characterize the change of the outlet flow rate over time under the actual or designed inlet turbidity; The second curve fitting module is used to obtain a water quality prediction curve based on the water flow prediction curve and the HYDRUS model. The water quality prediction curve is used to characterize the change of the outlet turbidity over time under the actual or designed inlet turbidity in the water flow prediction curve.

[0011] In addition, to achieve the above-mentioned purpose, the present application also proposes a simulation prediction device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the simulation prediction method described above.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the simulation prediction method described above are implemented.

[0013] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the simulation prediction method described above.

[0014] One or more technical solutions proposed in this application have at least the following technical effects: First, the preset model is used to simulate the outlet flow rate based on the actual or designed inlet turbidity and the corresponding partial outlet flow rate monitoring data. Then, the water flow prediction curve corresponding to the predicted outlet flow rate is input into the HYDRUS model as a change condition. The corresponding outlet turbidity (i.e., the water quality of the filter layer outlet water) under the current actual or designed inlet turbidity is predicted. The water flow prediction curve between the inlet turbidity, outlet flow rate and time, as well as the water quality prediction curve between the outlet turbidity, outlet flow rate and time are obtained, thereby realizing the simultaneous simulation and prediction of the water quantity and water quality of the water collection corridor project. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 A schematic diagram of a flow chart of a simulation prediction method provided in one embodiment of the present application is shown; Figure 2 A schematic diagram of a flow chart of a simulation prediction method provided by another embodiment of the present application is shown; Figure 3 shows a fitting curve of the target parameter T provided by an exemplary embodiment of the present application; Figure 4 shows a water flow prediction curve provided by an exemplary embodiment of the present application; Figure 5 shows a water quality prediction curve provided by an exemplary embodiment of the present application; Figure 6 A schematic diagram of the structure of a simulation prediction device provided in one embodiment of the present application is shown; Figure 7 A structural diagram of a simulation prediction device provided in one embodiment of the present application is shown.

[0018] The purpose, features and advantages of this application will be further explained with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0020] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0021] The main solution of the embodiment of the present application is: based on the actual or designed inlet turbidity and the corresponding partial outlet flow rate monitoring data, a water flow prediction curve is obtained, and the water flow prediction curve is used to characterize the change of the outlet flow rate over time under the actual or designed inlet turbidity; based on the water flow prediction curve and the HYDRUS model, a water quality prediction curve is obtained, and the water quality prediction curve is used to characterize the change of the outlet turbidity over time under the actual or designed inlet turbidity.

[0022] Among related technologies, under the dual influence of climate change and water demand from human activities, water collection corridor technology has gradually been widely used in various forms in water supply projects in water-scarce areas. This technology collects rainwater, floodwater, snowmelt, river water, or lake water, etc., after infiltration and filtration, for domestic or production use, especially in water-scarce areas and rural areas. Water collection corridor technology mainly utilizes the filtration function of the created infiltration system (riverbed aquifer + artificial filter layer + water collection structure) to induce surface water or aquifer groundwater to seep into the water collection structure and then extract it to the surface. However, in actual engineering applications, due to the influence of factors such as surface water volume and turbidity, water collection corridor projects often have problems such as declining water output year by year and easy clogging or penetration of filter materials, which greatly reduces the operating efficiency and service life of water collection corridor projects.

[0023] In order to better understand the operation effect of water collection corridor projects, some numerical simulation methods have also been widely used in the research of water collection corridor projects. For example, MODFLOW, Fluent and other models are used in related technologies to simulate engineering conditions.

[0024] However, the MODFLOW model can only be applied to saturated aquifers, while the hydraulic characteristics of the water collection gallery filtration system are mostly in an unsaturated state, so the simulation effect is not accurate enough; Fluent is also mostly used to simulate the flow field (water volume) inside the medium, which makes it difficult to apply in actual water collection gallery projects.

[0025] In summary, how to more accurately simulate the water quantity and water quality of water collection corridor projects is an urgent problem that needs to be solved.

[0026] Based on this, the present application provides a solution to simulate the outlet flow rate based on the actual or designed inlet turbidity and the corresponding partial outlet flow rate monitoring data, and then input the predicted water flow prediction curve into the HYDRUS model (for example, the HYDRUS-1D model) as a variable condition to predict the corresponding outlet turbidity, thereby realizing the simultaneous simulation and prediction of the water quantity and water quality of the water collection corridor project.

[0027] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or a simulation prediction device capable of performing the above functions. The following uses the simulation prediction device as an example to illustrate this embodiment and the following embodiments.

[0028] Reference Figure 1 , Figure 1 The flowchart of the simulation prediction method provided by an embodiment of the present application is shown. The simulation prediction method can be applied to a simulation prediction device, including the following steps S110 to S120: Step S110 , obtaining a water flow prediction curve based on the actual or designed inlet water turbidity and the corresponding partial outlet water flow rate monitoring data.

[0029] The water flow prediction curve is used to characterize the change of the outlet flow rate over time under the actual or designed inlet turbidity and the initial outlet flow rate (part of the outlet flow rate monitoring data).

[0030] Influent turbidity refers to the level of turbidity in water entering a watershed and is a key indicator of water clarity. Turbidity reflects the degree to which suspended matter in the water, such as soil, sand, fine organic and inorganic matter, plankton, microorganisms, and colloids, blocks light from passing through it.

[0031] Understandably, water collection corridor technology is mostly used to collect rainwater, floodwater, snowmelt, river water, or lake water after infiltration and filtration. Therefore, water often carries impurities such as sediment, and the amount of impurities carried by water can vary depending on precipitation conditions (e.g., light rain, heavy rain, flooding, etc.). Because this simulation is of a water collection corridor project, the actual or designed inlet water turbidity can be the user-defined pre-infiltration turbidity level based on actual simulation requirements, or the user-measured turbidity level in real-world scenarios.

[0032] The outflow velocity in a water collection corridor refers to the rate of water flow at the corridor's outlet. It's understood that water collection corridors typically only begin to function after rain, snow, or floods. The initial time can be set to the moment when water first penetrates the filter media. Users can customize the precipitation or water flow corresponding to rain, snow, or floods based on simulation requirements. In other words, the initial outflow velocity is the value set by the user based on actual simulation needs.

[0033] Taking precipitation as an example, it is understandable that as the precipitation process ends, the amount of water infiltrating into the catchment corridor gradually decreases over time. The purpose of water volume simulation is to more accurately predict the change of the outlet flow rate over time in the subsequent water collection process under different precipitation conditions, given the known influent turbidity and initial outlet flow rate.

[0034] In some embodiments, actual or designed inlet turbidity and corresponding partial outlet flow rate monitoring data can be input into a water flow prediction model to obtain a water flow prediction curve, wherein the water flow prediction model is obtained by screening multiple curve prediction models based on sample data.

[0035] The water flow prediction model refers to a mathematical relationship that describes the relationship between the change in influent turbidity and time using certain mathematical language. In some embodiments, to ensure the accuracy of the prediction results, the model's fit can be pre-evaluated using the coefficient of determination.

[0036] Goodness of fit refers to the degree of agreement between the curve fitted by the model and the actual test data. Goodness of fit is usually quantified using the coefficient of determination (R²), which ranges between 0 and 1. The closer R² is to 1, the better the model fit; the closer R² is to 0, the worse the model fit.

[0037] Specifically, to ensure that the fitting degree of the water flow prediction model meets the requirements, such as Figure 2 As shown, in some embodiments, the water flow prediction model can be determined through the following steps S210 to S230.

[0038] Step S210: Input the sample inlet water turbidity into a plurality of curve prediction models to obtain a sample prediction curve corresponding to each curve prediction model.

[0039] Curve prediction models are a type of statistical model used to fit and predict a series of data points, often exhibiting nonlinear relationships. Curve prediction models can capture complex dynamics in the data, such as growth or decay.

[0040] In some embodiments, a curve regression model in pre-selected statistical analysis software (e.g., SPSS software, etc., which is not limited herein) can be used to analyze the fit of multiple curve prediction models. For example, the multiple curve prediction models may include a univariate linear model, a logarithmic function model, a quadratic function model, a cubic function model, an exponential function model, and the like.

[0041] Among them, the univariate linear model can be expressed as: ; The logarithmic function model can be expressed as: ; The quadratic function model can be expressed as: ; The cubic function model can be expressed as: ; The exponential function model can be expressed as: .

[0042] In some embodiments, curve fitting can be performed using the aforementioned multiple curve prediction models based on sample data. Specifically, the sample data may include the sample inlet turbidity and the sample outlet flow rate. The sample inlet turbidity refers to the inlet turbidity actually measured by the user for the sample; the sample outlet flow rate refers to the outlet flow rate actually measured by the user for the sample at multiple time points.

[0043] Step S220 , for each sample prediction curve, calculate the water flow fitting determination coefficient according to the sample prediction curve and the sample water outlet flow rate.

[0044] After fitting multiple sample prediction curves, the statistical analysis software can automatically output the fitting determination coefficient corresponding to each sample prediction curve, namely R².

[0045] Specifically, the coefficient of determination can be calculated using the following expression:

[0046] Among them, SSR refers to the residual sum of squares, which can be calculated by the following expression:

[0047] in, Refers to the predicted value of the water flow rate corresponding to the sample prediction curve at time i, It refers to the sample water outlet flow rate corresponding to time i.

[0048] SST refers to the total sum of squares and can be calculated using the following expression:

[0049] in, It refers to the average water outlet flow rate of the sample.

[0050] In some embodiments, a screening threshold can be set to eliminate curve prediction models corresponding to sample prediction curves with a coefficient of determination below the screening threshold, and then select any one of the remaining curve prediction models as the water flow prediction model. For example, the screening threshold can be 0.97, that is, curve prediction models corresponding to coefficients of determination below 0.97 are directly eliminated.

[0051] In other embodiments, to further ensure the accuracy of the prediction, a water flow prediction model may be determined in step S230.

[0052] Step S230 , determining the curve prediction model corresponding to the sample prediction curve corresponding to the highest water flow fitting determination coefficient as the water flow prediction model.

[0053] Based on the above steps S210 to S230, the most accurate prediction model is determined from the multiple curve prediction models as the water flow prediction model, thereby ensuring the accuracy of the subsequent prediction of the outflow flow rate.

[0054] In some embodiments, the water flow prediction model is determined to be an exponential function model. Then the expression of the water flow prediction model is:

[0055] in, for The water flow rate corresponding to the moment, is the pre-determined initial water flow rate (i.e., the corresponding partial water flow rate monitoring data), e is the Napier constant, For time, It is a target coefficient that is positively correlated with the actual or designed influent turbidity.

[0056] Based on the expression, when predicting the outlet flow rate, it is only necessary to determine T through the actual or designed inlet turbidity and determine the initial outlet flow rate. , you can substitute it into the above formula to get the corresponding water flow prediction curve.

[0057] Therefore, in order to obtain the target coefficient T, the sample data can be used for fitting and the correlation between T and the influent turbidity can be calculated. Figure 3 As shown, based on the sample data, the coefficients and constants in the polynomial can be determined. For example, T can be calculated by the following expression:

[0058] in, is the actual or designed influent turbidity, is the fitting coefficient, and 0.0067 is the fitting constant.

[0059] As an example, Figure 4 As shown in the figure, using measured sample data and the above expression, we obtain flow prediction curves for different influent turbidities. The curves from top to bottom correspond to initial influent turbidities of 800 Nephelometric Turbidity Units (NTU), 1500 NTU, 3000 NTU, and 5000 NTU, respectively. It can be seen that as the initial influent turbidity increases, the time required for the effluent flow rate to decrease to near zero decreases.

[0060] Step S120 : obtaining a water quality prediction curve based on the water flow prediction curve and the HYDRUS model.

[0061] HYDRUS is a Windows-based environmental simulation software designed to simulate water movement, solute transport, and heat conduction in variable-saturated porous media. It includes two-dimensional and three-dimensional finite element models capable of handling saturated and unsaturated water flows, as well as convection and diffusion of solutes.

[0062] The following is an explanation using the HYDRUS-1D model as a typical example: In related art, when calculating outlet turbidity using the HYDRUS-1D model, the calculation is typically based on the permeability coefficient of the aquifer. However, the permeability coefficient of the HYDRUS-1D model can only be set to a fixed value, while the actual water permeability is a time-varying curve, as described in step S110. This makes it difficult to accurately predict the outlet turbidity based directly on the permeability coefficient.

[0063] Therefore, this application accurately predicts the outlet flow rate of the water collection corridor based on step S110, and directly uses the outlet flow rate in the water flow prediction curve as the input of the HYDRUS-1D model to predict the outlet turbidity at the same time, thereby avoiding the above-mentioned problem of insufficient accuracy.

[0064] Specifically, before implementing step S110 and step S120 provided in this embodiment, the feasibility of step S120 may be verified using sample data.

[0065] ① The corresponding multiple sample outlet turbidities actually collected under the sample inlet turbidity can be obtained, and the multiple sample outlet turbidities correspond to different moments.

[0066] ② Based on the water flow prediction model determined in step S230 and the turbidity of the sample inlet water, a corresponding sample prediction curve can be predicted.

[0067] ③ Input the sample simulation curve into the HYDRUS-1D model to obtain the sample simulation curve.

[0068] ④ Calculate the fitting degree of the sample simulation curve (ie, the turbidity fitting determination coefficient) in the same manner as the determination coefficient in the above embodiment.

[0069] ⑤ If the turbidity fitting determination coefficient is greater than the set value, it is determined that the HYDRUS-1D model can well predict the outlet turbidity using the outlet flow rate as input, that is, step S120 in this embodiment can be executed after step S110.

[0070] The set value may also be set to 0.97, which is not limited in this embodiment.

[0071] In actual experiments, Figure 5 As shown in the figure, the corresponding water quality prediction curves were finally obtained under the conditions of initial turbidity of 800, 1500, 3000, and 5000 NTU, respectively. The turbidity fitting determination coefficients and the turbidity of the sample water were calculated based on the water quality prediction curves and the sample effluent turbidity, and it was determined that the turbidity fitting determination coefficients were all greater than 0.98, that is, the fit met the prediction requirements.

[0072] Therefore, when making subsequent predictions, after obtaining the water flow prediction curve based on step S110, the water flow prediction curve can be directly input into the HYDRUS-1D model to obtain the corresponding water quality prediction curve, thereby achieving simultaneous prediction of water quality and water quantity.

[0073] This embodiment provides a simulation prediction method. First, a preset model is used to simulate the outlet flow rate based on the actual or designed inlet turbidity and the corresponding partial outlet flow rate monitoring data. Then, the water flow prediction curve corresponding to the predicted outlet flow rate is input into the HYDRUS-1D model as a variation condition. The corresponding outlet turbidity (i.e., the water quality of the filter layer outlet water) under the current actual or designed inlet turbidity is predicted. The water flow prediction curve between the inlet turbidity, outlet flow rate and time, and the water quality prediction curve between the outlet turbidity, outlet flow rate and time are obtained, thereby achieving simultaneous simulation and prediction of the water quantity and water quality of the water collection corridor project.

[0074] This application also provides a simulation prediction device, please refer to Figure 6 , the simulation prediction device 100 includes: A first curve fitting module 110 is used to obtain a water flow prediction curve based on the actual or designed inlet turbidity, where the water flow prediction curve is used to represent the change of the outlet flow rate over time under the actual or designed inlet turbidity; The second curve fitting module 120 is used to obtain a water quality prediction curve based on the water flow prediction curve and the HYDRUS model. The water quality prediction curve is used to represent the change of the outlet turbidity over time under the actual or designed inlet turbidity in the water flow prediction curve.

[0075] The simulation prediction device 100 provided in this application, employing the simulation prediction method of the aforementioned embodiment, can solve the technical problem of more accurately simulating the water quantity and quality of a water collection corridor project. Compared to the prior art, the beneficial effects of the simulation prediction device 100 provided in this application are the same as those of the simulation prediction method provided in the aforementioned embodiment. The other technical features of the simulation prediction device 100 are the same as those disclosed in the aforementioned embodiment and are not further described here.

[0076] The present application provides a simulation prediction device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the simulation prediction method in the above-mentioned embodiment one.

[0077] Reference below Figure 7 , which shows a schematic diagram of the structure of a simulation prediction device suitable for implementing the embodiments of the present application. The simulation prediction device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The simulation prediction device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0078] like Figure 7As shown, the simulation prediction device 200 may include a processing device 210 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 220 or programs loaded from a storage device 230 into a random access memory (RAM) 240. RAM 240 also stores various programs and data required for the operation of the simulation prediction device. Processing device 210, ROM 220, and RAM 240 are interconnected via a bus 250. An input / output (I / O) interface 260 is also connected to the bus. Typically, the following systems may be connected to I / O interface 260: input devices 270, such as a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 280, such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 230, such as a magnetic tape, hard disk, etc.; and communication device 290. Communication device 290 can allow the simulation prediction device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a simulation prediction device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have instead.

[0079] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 230, or installed from a ROM 220. When the computer program is executed by the processing device 210, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0080] The simulation prediction device provided in this application, using the simulation prediction method described in the above embodiment, can solve the technical problem of more accurately simulating the water volume and water quality of water collection corridor projects. Compared with the prior art, the beneficial effects of the simulation prediction device provided in this application are the same as those of the simulation prediction method described in the above embodiment. The other technical features of the simulation prediction device are the same as those disclosed in the above embodiment and are not further described here.

[0081] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0082] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0083] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, and the computer-readable program instructions are used to execute the simulation prediction method in the above-mentioned embodiment.

[0084] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0085] The computer-readable storage medium may be included in the simulation prediction device, or may exist independently without being incorporated into the simulation prediction device.

[0086] The computer-readable storage medium carries one or more programs that, when executed by the simulation prediction device, enable the simulation prediction device to write computer program code for performing the operations of the present application in one or more programming languages, or a combination thereof. The programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0087] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0088] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0089] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned simulation and prediction method. This computer-readable storage medium can address the technical problem of more accurately simulating the water quantity and quality of water collection corridor projects. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the simulation and prediction method provided in the aforementioned embodiments and are not further elaborated here.

[0090] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned simulation prediction method when executed by a processor.

[0091] The computer program product provided in this application can solve the technical problem of how to more accurately simulate the water quantity and water quality of a water collection corridor project. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the simulation prediction method provided in the above embodiment, and will not be repeated here.

[0092] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A simulation prediction method, characterized in that: The simulation prediction method comprises: According to the actual or designed inlet turbidity and the corresponding part of the outlet flow rate monitoring data, a water flow prediction curve is obtained, and the water flow prediction curve is used to characterize the change of the outlet flow rate over time under the actual or designed inlet turbidity; According to the water flow prediction curve and the HYDRUS model, a water quality prediction curve is obtained, and the water quality prediction curve is used to characterize the change of the outlet turbidity over time under the actual or designed inlet turbidity.

2. The simulation prediction method according to claim 1, characterized in that: The method of obtaining a water flow prediction curve according to the actual or designed influent turbidity includes: A water flow prediction curve is obtained based on the partial outlet water flow rate monitoring data under the actual or designed inlet water turbidity, wherein the water flow prediction curve is fitted in a curve regression model based on the partial outlet water flow rate monitoring data.

3. The simulation prediction method according to claim 2, characterized in that: When the curve regression model is an exponential function model, the expression of the curve regression model is: ； in, for The water flow rate corresponding to the time, is the initial outlet flow rate under the actual or designed inlet turbidity, e is the Napier constant, For time, It is the target coefficient that is positively correlated with the actual or designed influent turbidity.

4. The simulation prediction method according to claim 3, characterized in that: The target coefficient is obtained by the following expression: ； in, is the actual or designed inlet turbidity.

5. The simulation prediction method according to claim 4, characterized in that: Before obtaining the water quality prediction curve according to the water flow prediction curve and the HYDRUS model, the method further includes: Obtaining effluent turbidity monitoring data under the actual or designed influent turbidity; The water flow prediction curve under the actual or designed inlet turbidity is input into the HYDRUS model, and combined with the effluent turbidity monitoring data under the actual or designed inlet turbidity, the effluent turbidity simulation curve under the actual or designed inlet turbidity is obtained.

6. A simulation prediction device, characterized in that: The simulation prediction device comprises: A first curve fitting module is used to obtain a water flow prediction curve according to the actual or designed inlet turbidity, where the water flow prediction curve is used to characterize the change of the outlet flow rate over time under the actual or designed inlet turbidity; The second curve fitting module is used to obtain a water quality prediction curve based on the water flow prediction curve and the HYDRUS model. The water quality prediction curve is used to characterize the change of the outlet turbidity over time under the actual or designed inlet turbidity in the water flow prediction curve.

7. A simulation prediction device, characterized in that: The simulation prediction device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the simulation prediction method according to any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the simulation prediction method according to any one of claims 1 to 5 are implemented.

9. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the simulation prediction method according to any one of claims 1 to 5 are implemented.

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